The main idea for the course is to seek a deeper and more theoretical understanding of some practically useful techniques for modeling and estimation in engineering design. The class will draw from system identification, system modeling theory, statistics and data science disciplines in order to "let the data speak about the system." Prerequisites: ENGR 205, EE 263, AA 212. We will not use any specific materials covered in these subjects, but we assume basic background knowledge of state space, transfer functions, frequency responses, probability, and linear algebra. Intermediate Proficiency in Matlab is preferred (but Python is OK).
3 units · Letter (ABCD/NP)
The main idea for the course is to seek a deeper and more theoretical understanding of some practically useful techniques for modeling and estimation in engineering design. The class will draw from system identification, system modeling theory, statistics and data science disciplines in order to "let the data speak about the system." Prerequisites: ENGR205, EE263, AA212. We will not use any specific materials covered in these subjects, but we assume basic background knowledge of state space, transfer functions, frequency responses, probability, and linear algebra. Intermediate Proficiency in Matlab is preferred (but Python is OK).
Offered in Spring 2027 at Stanford University.